A variation of ANNs, highly popular for classification tasks and widely used for image recognition, is the CNNs [1]}. The input to a CNN is typically a 3-dimensional tensor, instead of input vectors used in the standard ANN. For example, for image classification tasks, each input layer of the data contains pixel values for each of the three RGB color channels in the form of a 2-dimensional matrix. Stacking these three matrices together will form the 3-dimensional input tensor. The same procedure is followed when grey-scale images are used as input [2]}, [3]}. In the context of propagation modeling, the input to the CNN does not have to be visual, i.e. instead of color intensities, each pixel can represent other useful information. Moreover, a varying number of input layers/channels can be used. For example, in [4]}, the authors used two input channels. One, encoded the normalized height of each building in an RT-generated city grid. The other, accounted for the normalized difference in height between the transmitter and the ground level at each point of the grid. In [5]}, the authors created a square grid representing an urban square that was modeled in RT. They used three channels containing information regarding the distance from the transmitter, the distance from the receiver, as well as the height of each cell on the grid, respectively. Three layers of input features were also used in [6]}. Furthermore, the input is not required to contain spatial information. For example, in [7]}, the authors used images of RSS coming from multiple APs for a given indoor location. Instead of using an input vector containing these values, they converted them into a 2-dimensional format by zero-padding. Some of the most popular CNN implementations in computer vision have also been used for propagation modeling. Among them are VGG-16 [8]} as well residual network (ResNets) implementations [9]}. CNNs use filters (feature detectors) to learn mappings between the input and output space. A parameter sharing scheme exists among filters, i.e. the same filter is applied at different parts of the input image [1]}. That trait as well as sparsity of connections make CNNs more efficient than standard MLPs [1]}.
